SLIITThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.http://digitallibrary.sliit.lk:802017-03-28T15:57:57Z2017-03-28T15:57:57ZDeep Object Vision Convolutional Neural NetworkKarunarathne, P.G.U.D.MBamunusinghe, J.http://hdl.handle.net/123456789/3762017-03-27T10:37:41Z2016-01-01T00:00:00ZTitle: Deep Object Vision Convolutional Neural Network
Authors: Karunarathne, P.G.U.D.M; Bamunusinghe, J.
Abstract: Deep learning has achieved great heights in terms of accuracy in large-scale image classification task in the recent past with the introduction of Convolutional Neural Network (CNN) model. We propose an improved version of CNN model based on the Alexnet, called Deep Object Vision (DeepOV). The proposed model was designed and implemented in Python environment using Theano backend and Keras libraries for the efficient GPU utilization. DeepOV model uses the CIFAR-10 dataset for training testing and validation purposes and demonstrates better accuracy rates in comparison to the classical object recognition models. We achieved 80% validation accuracy on test set, overwhelming the common drawbacks that exist in Alexnet model. The proposed DeepOV model has 26 million parameters including four convolutional layers, some of which are followed by max-pooling layers, and two fully connected layers and finally a softmax layer. We introduce data preprocessing, image whitening and regularization techniques to further improve the recognition accuracy of the proposed model.2016-01-01T00:00:00ZContext Rich Hybrid Navigation Using Wi-Fi And Geomagnetic Sensors in Smartphones and Map Generation Using Lidar : IPSAlahakoon, D.Somathilake, P.Weerawardene, N.ayakody, A.http://hdl.handle.net/123456789/3752017-03-27T08:10:38Z2016-01-01T00:00:00ZTitle: Context Rich Hybrid Navigation Using Wi-Fi And Geomagnetic Sensors in Smartphones and Map Generation Using Lidar : IPS
Authors: Alahakoon, D.; Somathilake, P.; Weerawardene, N.; ayakody, A.
Abstract: Navigation systems perform a huge role in
traveling component of life. Most importantly it helps people get
to places even in foreign or unfamiliar environments. Even
though there are well-groomed systems to navigate outdoors,
there is no proper way of navigating indoors. People do most of
their activities, business, commerce, entertainment and
socializing indoors. As the growing development and expansions
of these indoor establishments, it has become much more
complex to find places of interest, hence the urgency for a costeffective,
accurate and more generalized solution for indoor
navigation has arisen. This research, we present a complete
Indoor Positioning System, coined IPS, which has an open,
modular, extensible system architecture, which makes it
superlative for a wide range of applications.
This research introduces a way of mapping environments
with less effort, it uses a technology called ‘LIDAR,’ which is
clearly feasible for the requirement of indoor map generation.
IPS is implemented with a set of crowdsourcing-supportive
mechanisms to handle the collective amount of raw data, filter
incorrect user contributions and rouge Wi-Fi data from diverse
mobile devices. Furthermore, it uses a big-data architecture for
effective storage and retrieval of localization and mapping data.
In this research, presented service relies on the sensitive data
collected by smartphones (Wi-Fi signal strength and geomagnetic
measurements) to deliver reliable indoor geolocation
information.2016-01-01T00:00:00ZCloud Based Secure PAC System to Analyze DICOM ImagesJohnson, R.Paktharatshan, S.Sooriyakumaran, S.Siluvaithasan, T.Pulasinghe, P.Weerawarna, N.T.http://hdl.handle.net/123456789/3742017-03-27T08:06:23Z2016-01-01T00:00:00ZTitle: Cloud Based Secure PAC System to Analyze DICOM Images
Authors: Johnson, R.; Paktharatshan, S.; Sooriyakumaran, S.; Siluvaithasan, T.; Pulasinghe, P.; Weerawarna, N.T.
Abstract: Picture archiving and Communication System (PACS) is the backbone of the analysis of medical images as it is well adopt with Digital Imaging and Communications in Medicine(DICOM) standard. In this paper 3D modeling of brain to detect diseases more accurately, content based image retrieval for medical image analysis, brain tumor segmentation for area calculation of tumor and cloud deployment and DICOM image security using Advanced Encryption Standard (AES) encryption for secure remote working have been added to overcome the issues in the existing PAC system implemented by the past students of Sri Lanka Institute of Information Technology (SLIIT)2016-01-01T00:00:00ZBringOWN - AN AGILE BYOD SOLUTION WITH DEVICE FRIENDLY CIPHERINGThisera, W.K.T.Bandara, D.Vithana, P.Shavindrika, S.http://hdl.handle.net/123456789/3732017-03-27T07:51:40Z2016-01-01T00:00:00ZTitle: BringOWN - AN AGILE BYOD SOLUTION WITH DEVICE FRIENDLY CIPHERING
Authors: Thisera, W.K.T.; Bandara, D.; Vithana, P.; Shavindrika, S.
Abstract: Recently, BYOD or Bring Your Own Device has become one of the most popular model for enterprises to provide mobility and flexibility in workplaces. The emergence of new technologies and features of mobile devices makes them integral parts of every aspect of daily business activities. In BYOD, the personal devices can be used to increase employees’ satisfaction and reduce an organization’s device costs. However, due to attacks and vulnerabilities it is difficult to trust the personal devices coming into the workplace. There is security concern to protect sensitive corporate data, protecting sensitive corporate data is a major concern in the industry. This project proposal proposes a BYOD model which can be used to overcome the mention problems /vulnerabilities. The BYOD model is a combination of a light weight.2016-01-01T00:00:00Z